Implementation of Swarm Intelligence Algorithms for Path Planning

Abstract

The ability to navigate through an unknown terrain is an essential metric in evaluating the performance of multi-robot systems. With the use of swarm intelligence algorithms like Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), even robots with limited capability can perform this task. In this paper, we implement ACO and PSO to solve the path planning problem, and we also compare their efficiencies in different environments. This paper also implements a new hybrid algorithm that utilizes both ACO and PSO for more efficient path planning.